A Survey on Value Alignment in Agentic AI Systems

Abstract

With the evolution of artificial intelligence (AI) paradigms towards agentic AI, the widespread integration of large language models (LLMs) enhances system capabilities while also introducing situational risks and challenges of value misalignment, making value alignment in agentic AI systems a critical issue. This paper constructs a multi-level value framework encompassing L0 (universal values), L1 (cultural and industry values), and L2 (context-specific values). Guided by this framework, we conduct an in-depth analysis along the technical stack: at the LLM level, we examine value injection mechanisms through pretraining and post-training; at the single-agent level, we focus on representation and injecting values to agents, Profiles and memory, and planning and action; at the multi-agent level, we summarize collaborative alignment methods such as communication strategy optimization and multi-objective reinforcement learning. Following a systematic review of existing datasets and methods for multi-level alignment evaluation, we outline future research directions, including inter-agent value coordination mechanisms, high-quality scenario data sharing, game-theoretic design for value alignment in agent interaction and communication protocol alignment—aiming to establish a more systematic and dynamic evaluation framework and to promote robust and trustworthy value consensus in agentic AI systems within social collaboration.